Quick Start¶
Run the pipeline with test data to verify your installation.
Prerequisites¶
Complete the Installation guide first:
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Dry Run¶
Preview what the pipeline will do without executing:
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Expected output:
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Run Test Pipeline¶
Execute the pipeline locally with 4 cores:
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This processes two test samples and takes approximately 15-30 minutes depending on your hardware (GPU required).
GPU Required
The rebasecall and classify_charging rules require GPU access. Ensure CUDA is available.
Expected Output¶
After completion, outputs are in .tests/outputs/:
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Inspect Key Outputs¶
Charging CPM Table¶
View per-tRNA charging counts:
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Output columns:
| Column | Description |
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| tRNA | tRNA reference name |
| counts_charged | Number of charged reads |
| counts_uncharged | Number of uncharged reads |
| cpm_charged | Charged counts per million |
| cpm_uncharged | Uncharged counts per million |
Per-Read Charging Probabilities¶
View individual read classifications:
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Output columns:
| Column | Description |
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| read_id | Nanopore read ID |
| tRNA | Aligned tRNA reference |
| charging_likelihood | ML score (0-255; ≥200 = charged) |
Final BAM Tags¶
The final BAM contains charging classification in tags:
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CL:B:C- Charging likelihood (ML tag renamed to avoid conflict)CM:Z- Charging model metadata (MM tag renamed)PT:Z- Adapter positions (5' and 3' adapter boundaries)
Run on Cluster¶
For cluster execution with LSF:
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Or manually with a profile:
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See LSF Setup for cluster configuration.
Cleanup¶
Remove test outputs:
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Next Steps¶
- First Analysis - Use your own data
- Configuration - Customize parameters
- Output Files - Detailed output documentation